Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models
2026-03-31 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors created a new system that lets robots understand and follow complex instructions better by combining two methods: Reinforcement Learning (RL) for precise movements and Large Language Models (LLMs) for planning and language understanding. They tested their system in a simulated environment with a robot arm and found it completed tasks faster and more accurately than using RL alone. This shows their approach helps robots be smarter and more adaptable. They plan to make it work in real-world settings and with multiple robots in the future.
Reinforcement LearningLarge Language ModelsRobotic ManipulationTask PlanningNatural Language UnderstandingPyBullet SimulationFranka Emika PandaSim-to-Real TransferRobot Adaptability
Authors
Md Saad, Sajjad Hussain, Mohd Suhaib
Abstract
This paper introduces a new hybrid framework that combines Reinforcement Learning (RL) and Large Language Models (LLMs) to improve robotic manipulation tasks. By utilizing RL for accurate low-level control and LLMs for high level task planning and understanding of natural language, the proposed framework effectively connects low-level execution with high-level reasoning in robotic systems. This integration allows robots to understand and carry out complex, human-like instructions while adapting to changing environments in real time. The framework is tested in a PyBullet-based simulation environment using the Franka Emika Panda robotic arm, with various manipulation scenarios as benchmarks. The results show a 33.5% decrease in task completion time and enhancements of 18.1% and 36.4% in accuracy and adaptability, respectively, when compared to systems that use only RL. These results underscore the potential of LLM-enhanced robotic systems for practical applications, making them more efficient, adaptable, and capable of interacting with humans. Future research will aim to explore sim-to-real transfer, scalability, and multi-robot systems to further broaden the framework's applicability.